In document processing, the use of bar codes on documents to identify and index documents is becoming wide spread. Industries such as insurance, package shipping, health care and litigation require documents to be identified, categorized and routed to their appropriate destinations.
Bar codes are designed to be read by computers. A bar code is a self-contained message whose information is encoded in the widths or relative widths of its printed bars and spaces. When a bar code is read, the patterns of light and dark contained in the bars and spaces are translated into patterns of ones and zeros which the computer interprets as numeric or alphanumeric data. Imaging automation by an image scanner which converts a document's image, including its bar code, into a bit map can make high volume document processing more time efficient and cost effective. High volume document imaging systems are routinely called upon to efficiently capture, store, manipulate, and retrieve hundreds of millions of document images.
Processing documents to identify or categorize them is a much more difficult problem than to process documents which have been pre-sorted. In processing documents to identify them, any type of document might be feed into a scanner. One or more bar codes therefore might be positioned in any number of locations on a document page. Thus, in order to read the bar code, the information on the document must be analyzed in order to locate the bar code. Current systems however, have limited facility to discriminate between text or other information on a page and a bar code in order to locate bar codes.
In processing documents which use more than one type of bar code, the ability to distinguish between types of bar codes in order to read them is necessary. Corrections to the bar code bit map data may be required prior to reading to correct, for example, skewing of the bar code. Moreover, if there is a loss of symbol data due to poor print quality, copying or faxing, bar and space width corrections need to be made before reading.
Document processing by imaging requires automatic reading from a largely uncontrolled input, i.e. the image of a page. Although systems currently used employ high-speed scanning hardware, they lack the adaptability to read bar codes which are not positioned in a routine location on a page or routine element widths. Moreover current bar code imaging systems often fail to read bar codes which are either skewed on a page or when a page is fed into the scanner at an angle.